Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data
Foody, G.M. and Cutler, M.E. (2002) Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data. In, Papers in proceedings of the IGARSS '02 conference. Geoscience and Remote Sensing Symposium, IGARSS 2002 IEEE International Piscataway, N.J., USA, IEEE, 497-499. (doi: 10.1109/IGARSS.2002.1025085).
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Description/Abstract
Two types of neural network were used to derive measures of biodiversity from Landsat TM data of a tropical rainforest. A feedforward neural network was used to estimate species richness while a Kohonen neural network was used to provide information on species composition. The results indicate the potential of remote sensing as a source of maps of biodiversity.
| Item Type: | Book Section |
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| Related URLs: | |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) |
| Divisions: | University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis |
| Item ID: | 15214 |
| Date Deposited: | 30 Mar 2005 |
| Last Modified: | 02 Mar 2012 11:44 |
| Contributors: | Foody, G.M. (Author) Cutler, M.E. (Author) |
| Date: | 2002 |
| Status: | Published |
| Publisher: | IEEE |
| URI: | http://eprints.soton.ac.uk/id/eprint/15214 |
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